Bioaccumulation Screening of Neutral Hydrophobic Organic Chemicals in Air-Breathing Organisms Using In Vitro Rat Liver S9 Biotransformation Assays
Bibliographic record
Abstract
Abstract To advance methods for bioaccumulation assessment of organic substances in air-breathing organisms, the present study developed an in vitro approach for screening neutral hydrophobic organic substances for their bioaccumulation potential in air-breathing organisms consisting of (1) depletion assays for chemicals in rat liver S9 subcellular fractions, (2) in vitro–in vivo extrapolation, and (3) whole-organism bioaccumulation modeling to assess the biomagnification potential of neutral organic substances in the rat. Testing of the in vitro method on 14 test chemicals of potentially biomagnifying substances showed that the bioassays could be conducted with a high level of reproducibility and that in vitro–derived elimination rate constants were in good agreement with in vivo–determined elimination rate constants in the rat. Exploring the potential of the in vitro approach for screening organic chemicals for bioaccumulation in air-breathing organisms indicated that chemical substances that exhibit a depletion rate constant in the S9 in vitro bioassay ≥0.3 h−1 are not expected to biomagnify in rats independent of their octanol–water partitioning coefficient (KOW) or octanol–air partitioning coefficient (KOA). The high level of reproducibility achieved in the test, combined with the good agreement between in vitro–derived and in vivo–determined depuration rates, suggests that the in vitro approach in combination with a KOA- and KOW-based screening approach has good potential for screening chemicals in commerce for their bioaccumulation potential in air-breathing organisms in a cost-effective and expedient manner, especially if the bioassay can be automated. Environ Toxicol Chem 2022;41:2565–2579. © 2022 SETAC
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".